Uploaded June 2026 | Updated September 2026, 8 hours ago
Self-Balancing Robot Control using LQR (MATLAB Simulink + Simscape)
How do engineers stabilize an inherently unstable system in real time?
The model includes full nonlinear system dynamics, state-space control design, real-time simulation, and performance visualization.
⚙️ Project Highlights:
✔ Nonlinear self balancing robot modeling
✔ Simulink + Simscape physical system implementation
✔ LQR optimal control design
✔ Full state analysis (angle, position, velocity)
✔ MATLAB-based animation and visualization
✔ Performance evaluation using RMSE and stability metrics
📊 Key Results:
LQR Controller
Angle RMSE: 0.0076 rad
Position RMSE: 0.0038 m
Max Tilt: 1.29°
Stable, fast, and smooth response
🧠 Key Insight:
LQR uses optimal state-feedback control to minimize both system error and control effort
For unstable and underactuated systems like self balancing robots, LQR provides significantly superior stability and performance.
💡 Result: Optimal control outperforms classical tuning in dynamic robotic systems.
🔥 Repost this post if you’re into robotics and control systems.
👇 Comment “LQR_robot” to get the full project (Code + Simulink + Report + PPT)
#MATLAB #Simulink #Simscape #ControlSystems #Robotics #LQR #PIDControl #SelfBalancingRobot #InvertedPendulum #Mechatronics #Automation #EngineeringSimulation #FYP #FinalYearProject #RoboticsEngineering #STEM
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Self-Balancing Robot Control using LQR (MATLAB Simulink + Simscape)
How do engineers stabilize an inherently unstable system in real time?
The model includes full nonlinear system dynamics, state-space control design, real-time simulation, and performance visualization.
⚙️ Project Highlights:
✔ Nonlinear self balancing robot modeling
✔ Simulink + Simscape physical system implementation
✔ LQR optimal control design
✔ Full state analysis (angle, position, velocity)
✔ MATLAB-based animation and visualization
✔ Performance evaluation using RMSE and stability metrics
📊 Key Results:
LQR Controller
Angle RMSE: 0.0076 rad
Position RMSE: 0.0038 m
Max Tilt: 1.29°
Stable, fast, and smooth response
🧠 Key Insight:
LQR uses optimal state-feedback control to minimize both system error and control effort
For unstable and underactuated systems like self balancing robots, LQR provides significantly superior stability and performance.
💡 Result: Optimal control outperforms classical tuning in dynamic robotic systems.
🔥 Repost this post if you’re into robotics and control systems.
👇 Comment “LQR_robot” to get the full project (Code + Simulink + Report + PPT)
#MATLAB #Simulink #Simscape #ControlSystems #Robotics #LQR #PIDControl #SelfBalancingRobot #InvertedPendulum #Mechatronics #Automation #EngineeringSimulation #FYP #FinalYearProject #RoboticsEngineering #STEM
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